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Key Takeaways |
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54% of enterprises can’t access real-time data. AI-powered engagement only works when the data foundation connects behavioral signals with what’s actually happening across the business. 78% say AI is essential. 77% can’t use it. Most can’t operationalize AI because it runs on campaign data alone. Operational data is the missing layer. Inventory, order status, and service events turn AI-powered engagement from a campaign tool into an enterprise capability. |
Leah finds the perfect limited-edition hair color at 11pm on a Sunday. She orders it before the shade sells out. By Tuesday morning, her phone is a hot mess:
Leah doesn’t know any of this is AI-powered. She just knows it feels broken. Three messages, none of them useful, all of them landing in the 36 hours after she gave the brand her money and her trust.
Most guides to AI-powered customer engagement focus on what AI can do: personalize, predict, automate. The deeper, more useful question is what data the AI has access to.
When engagement AI runs on behavioral data alone, it’s fast but blind to operational reality. When it’s connected to inventory, order status, fulfillment timelines, and service data, every decision reflects what the business can actually deliver.
In this guide we give you the strategies that make AI-powered customer engagement work in practice.
What follows is based on what 10,000 consumers and 4,800 enterprise decision-makers across six countries told us in the 2026 Global Engagement Index about where AI-powered engagement is working, where it’s falling short, and what separates brands getting results from the ones just running campaigns. So let’s dig in.
AI-powered customer engagement uses machine learning, predictive AI, generative AI, and AI agents to understand customers and deliver more relevant interactions across the customer lifecycle.
It’s worth separating AI from automation, because marketers use both and the terms get blurred:
Automation follows predefined rules: if a customer abandons a cart, send a reminder after two hours.
AI interprets data, identifies patterns, and makes or recommends decisions without being explicitly programmed for each scenario. It’s capable of learning that a customer who abandons three carts in a week doesn’t need three reminders. AI autonomously recognizes the pattern and adapts.
Combining the two allows customer journeys to respond to individual behavior and context rather than following a fixed sequence. That’s the promise. The question is whether the data behind it is good enough to deliver on it.
A head of CRM rolls out an AI-powered personalization engine. Open rates climb, click-throughs improve, but then a customer contacts support asking why she received a “we miss you” offer an hour after placing a $200 order. That clever, new personalization engine was reading email engagement, but that order sitting in the ERP system was invisible to it.
This is the pattern across industries. According to the 2026 Global Engagement Index, 54% of enterprises can’t access and use real-time data. A full 60% suffer from dark data – information that’s collected but never activated. And 55% say their data is too unstructured to use effectively.
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Connected Challenges The data problems compounding the gap. |
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Source SAP Global Engagement Index 2026 |
At SAP Engagement Cloud we know that most AI-powered engagement has a data problem, not a capability problem.
Most solutions run AI on behavioral and campaign data: clicks, opens, browsing patterns, but that only gives you half the picture.
Without operational data – inventory levels, order status, fulfillment timelines, service history, payment signals – AI makes recommendations that are technically personalized but operationally disconnected.
Think:
Every one of those messages costs money to send as well as costing them the effort it takes to earn back their trust.
The same research confirms how wide this gap runs. While 77% of brands admit they can’t use AI to optimize campaign performance, 78% say AI is essential for retaining customers in 2026. The ambition is there but the infrastructure to support it, for most organizations, isn’t yet.
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The AI Readiness Gap Brands know AI is critical for retention. Most can’t operationalize it. |
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Source SAP Global Engagement Index 2026 |
Christian Wandel, Head of Performance Marketing at CHRIST, the largest jewelry retailer in Germany, describes the problem every mid-market brand eventually runs into:
“We had many CRM systems and silos for data collection. We wanted to be more efficient and improve communication with our customers to increase the repurchase rate.”
That’s a typical starting point. Multiple systems, fragmented customer records, and no single view of who bought what, when it shipped, and whether they’re happy. AI layered on top of that foundation generates confident-sounding outputs based on incomplete inputs.
Connected data means bringing together:
That last category is the one most AI-powered engagement solutions don’t have access to – and it’s the reason so many of them produce messages that feel tone-deaf to the customer on the receiving end.
The 2026 Global Engagement Index found that only 2 in 5 decision-makers believe their departments are truly coordinated. Fewer than 30% share their customer engagement data with a CX or CRM solution.
Once that foundation is connected, AI has something real to work with. The next three sections cover how.
Leah’s browsing pattern suggests she’s also interested in premium skincare. Another brand’s basic AI engine surfaces the best-selling serum.
A system connected to operational data checks stock levels, confirms next-day delivery is available, factors in her purchase history (she bought the entry-level version six months ago and hasn’t reordered), and surfaces the upgrade at the right price point with a replenishment reminder timed to her usage cycle.
That’s the difference between personalization that matches a product to a click and personalization that matches an offer to a customer’s actual situation.
AI-powered personalization at scale covers:
The 2026 Global Engagement Index shows the disconnect clearly. A full 58% of consumers value personalized product recommendations, and 55% appreciate highly personalized content. But 37% say brands don’t personalize to their needs. And 41% say brands don’t understand them as a person.
PUMA uses SAP Engagement Cloud to combine customer behavior with revenue insights, achieving 5X revenue from email in six months through predictive AI segmentation and 50% database growth by connecting AI-infused referral programs. That kind of result comes from AI that’s working with complete data, not guessing from partial signals.
Here’s how a rules-based system might handle this customer journey:
Alternatively, an AI-powered system is connected to service data and therefore suppresses the promotion. It sends a delivery update instead with a personalized offer on a related product that’s in stock and ready to ship tomorrow.
That decision required three data layers: behavioral data (the engagement score), service data (the delivery delay), and inventory data (what’s available right now). Most engagement strategies run on the first layer only.
Next-best-action decisioning means the AI evaluates multiple signals to determine the most relevant action for each customer at that moment. The options aren’t limited to “send” or “don’t send.” They include:
Churn signals are worth flagging specifically. AI can recognize combinations of declining engagement, reduced purchase frequency, and lower channel interaction before a customer fully lapses. That early detection turns re-engagement from a Hail Mary into a timely, relevant conversation.
That same research puts this in context. While 63% of consumers say their favorite brand delivers connected experiences across mobile, web, and in store, 75% are put off by disorganized brands that pass them between teams to solve a single problem. Consistency matters more than volume.
Ferrara, the sweet snacking company behind NERDS, Trolli, and Jelly Belly, unified scattered CRM systems into a single customer profile using SAP Engagement Cloud, then deployed personalized omnichannel engagement powered by real-time data. The result: a 59% increase in contactable customers, email open rates 20% above industry average, and 300% fan growth for the Trolli brand. That’s what connected execution looks like when AI has access to all that vital data on what the business can actually deliver.
“Agentic AI” has become the catch-all term in customer engagement, but most of the time it means a chatbot with a better vocabulary.
Joule is SAP’s AI across the enterprise, spanning assistants that coordinate work across business domains and agents that execute multi-step workflows autonomously. Humans set the strategy that the AI agents and assistants follow.
In the context of customer engagement, Joule Assistants coordinate two areas that consume the most marketing time:
Joule agents also perform multi-step workflows autonomously:
A joint Boston Consulting Group and SAP study on agentic AI impact for customer experience validated the results: 15% increase in revenue per campaign, 44% reduction in time spent on content creation, and 20% increase in ROI per channel.
The key distinction is where these assistants and agents sit. They’re embedded within the SAP ecosystem, which means they’re drawing on the same operational data – inventory, order status, fulfillment, service events – that runs the business. An AI agent recommending a product isn’t guessing from browsing history alone. It’s confirming availability, delivery timelines, and margin before surfacing the recommendation.
Molton Brown, the luxury personal care brand, uses SAP Commerce Cloud and SAP Engagement Cloud together. The connected data foundation delivered +22% year-over-year conversion during key campaigns and +30% sales over three years through AI-powered personalized engagement.
You know this meeting: Campaign dashboards show opens, clicks, and impressions all trending up. Then someone from finance asks why repeat purchase rate is flat. The AI-powered campaigns are performing by every campaign metric but they’re not moving business outcomes.
Measuring AI-powered engagement means looking past campaign metrics to business metrics:
Control groups matter here. Running an AI-powered journey alongside a holdout group that receives the standard experience is the only way to determine whether AI is actually lifting performance or just correlating with it.
The 2026 Global Engagement Index frames this as a maturity shift. High-maturity brands measure engagement by retention, loyalty, and lifetime value. Lower-maturity brands are still tracking opens and clicks. The question for brands who want AI-powered customer engagement to properly ‘work’ for them is whether it’s moving the numbers that the business reports on.
Let’s travel back in time to Leah. She’s making that Sunday night, semi-impulse hair color purchase. But this time the brand’s AI is connected to operational data. By Tuesday morning, she receives one message: A text thanking her for the order, confirming it shipped that morning, and recommending a color-protecting conditioner that pairs with her shade – in stock, arrives Thursday, and 15% off because she’s bought from the brand three times this year.
One message instead of three. Leah thinks: “They get me.”
AI-powered customer engagement works when AI decisions are grounded in what’s actually happening across the business. That means predictive intelligence built on connected data, Joule assistants and agents coordinating across marketing, commerce, sales, and service, and omnichannel execution informed by operational reality.
SAP Engagement Cloud connects real-time customer insights with the operational signals that run the business, turning AI from a campaign layer into an enterprise capability that drives retention, revenue, and measurable growth.
Marketing automation follows predefined rules: if a customer does X, trigger action Y. AI-powered customer engagement interprets data, identifies patterns, and recommends or takes actions based on context. Automation sends a cart abandonment email after two hours. AI recognizes that a customer who abandons carts repeatedly may need a different approach entirely, and adapts the journey accordingly.
AI improves customer engagement by making interactions more relevant and better timed. Predictive AI identifies which customers are likely to churn, what products they’re most likely to buy, and when they’re most receptive to messages. Generative AI scales content creation across channels and languages. AI agents can coordinate multi-step workflows – from building audience segments to adapting journeys based on real-time signals – so marketers focus on strategy while AI handles execution at scale.
AI-powered engagement performs best with a connected data foundation that includes behavioral data (browsing, clicks, email engagement), transactional data (purchase history, order value), zero-party data (stated preferences), and operational data (inventory levels, order status, fulfillment timelines, service events). According to the 2026 Global Engagement Index, 54% of enterprises still can’t access and use real-time data, which limits how effectively AI can personalize and optimize engagement.
Measure AI-powered engagement by business outcomes rather than campaign metrics. Key metrics include retention rate, repeat purchase rate, customer lifetime value, re-engagement rate from churn-risk segments, and conversion rate from AI-triggered journeys compared to manual campaigns. Use control groups to determine whether AI-powered experiences deliver incremental lift over existing approaches.
Joule is SAP’s AI across the enterprise. In the context of customer engagement, Joule assistants coordinate agents that generate and adapt creative assets at scale, build audience segments, activate campaigns, and surface performance insights. Joule agents perform multi-step workflows autonomously – adapting customer journeys based on real-time signals, surfacing product recommendations grounded in live inventory data, and building predictive audience segments. Because Joule operates within the SAP ecosystem, every decision draws on the same operational data that runs the business.
Pauline Passley is an Senior Solution Advisor at SAP who helps enterprise organizations leverage customer data, AI, and customer engagement strategies to accelerate growth and strengthen customer relationships. Her work focuses on the intersection of customer experience, go-to-market strategy, and business transformation, helping organizations turn technology investments into measurable business outcomes.
